[ 526DAKEMKD13 ] Module Methods and Concepts in Data & Knowledge Engineering

(*) Unfortunately this information is not available in english.
Workload Mode of examination Education level Study areas Responsible person Coordinating university
6 ECTS Accumulative module examination B2 - Bachelor's programme 2. year Business Informatics Michael Schrefl Johannes Kepler University Linz
Detailed information
Pre-requisites (*)Erwartete Vorkenntnisse: Grundlagen der Wirtschaftsinformatik, Grundlagen der Informatik, Grundlagen der Mathematik, Statistik und formaler Methoden
Original study plan Bachelor's programme Business Informatics 2022W
Objectives Students know the architecture, functionality and characteristics of database systems based on various data models, web-based information systems, and knowledge-based systems and systems based on symbolic and subsymbolic methods of artificial intelligence, and their typical application areas. They are familiar with methods and techniques of data and knowledge engineering and know distributed, temporal and object-oriented and non-relational concepts to design database systems. They are able to handle the exemplary application of object-relational and non-relational database systems as well as basic techniques of data warehousing and data mining. They are familiar with concepts and methods of knowledge-based systems and know typical application areas of ontologies and knowledge graphs and business rule engines.
Subject Functionality of database management systems: concurrency control, recovery; database technologies: distributed database systems, object-relational database systems, temporal database systems, NoSQL and NewSQL database systems, active database systems and business rule engines, deductive database systems; Fundamentals of Artificial Intelligence: knowledge representation and reasoning, heuristic search, constraint satisfaction, answer set programming, genetic algorithms, neural nets; web-based information systems: web query languages, use of semi-structured data on the web, semantic web/web of data; Business intelligence and analytics: data warehousing, OLTP vs OLAP, dimensional fact model, data mining, decision tree learning, association rules.
Further information A characteristic feature of the applied teaching form used here is working with an e-tutor system. Topics covered by the lecture are reinforced with working on practical examples. These examples are processed under the guidance of the lecturer by the students with appropriate tools. Solving these exercises is done online by the help of the electronic tutoring system “eTutor”. The “eTutor” system provides students with assistance in finding and fixing errors during the working process and handles the automatic assessment of the solution. Moreover, the automatic assessment is checked by the lecturers. The final assessment is made by the lecturer.
Subordinated subjects, modules and lectures